loading
Documents

Research.Publish.Connect.

Paper

Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data

Topics: Automotive Control and Mechatronics; Autonomous Vehicles and Automated Driving; Big Data & Vehicle Analytics; Big Data Analytics for Intelligent Transportation; Connected Services; Electric Vehicles; Fleet telematics; Public Transportation Management; Remote Diagnosis; Vehicle Telematics

Authors: Julia Hofmockel 1 and Eric Sax 2

Affiliations: 1 Audi Electronics Venture GmbH, Germany ; 2 Karlsruhe Institute of Technology, Germany

ISBN: 978-989-758-293-6

Keyword(s): Anomaly Detection, Data Transfer, Isolation Forest.

Abstract: A vehicle generates data describing its condition and the driver’s behavior. Sending data from many vehicles to a backend costs money and therefore needs to be reduced. The limitation to relevant data is inescapable. When using data collected from a vehicle fleet, the normality can be learned and deviations from it identified as abnormal and thus relevant. The idea of learning the normality with the Replicator Neural Network and the Isolation Forest is applied to the identification of anomalies and the reduction of data transfer. It is compared how good the methods are in detecting anomalies and what it means for the traffic between vehicle and backend. It can be shown that the Isolation Forest beats the Replicator Neural Network. When reducing the transfered amount of data to 7%, in average more than 80.63% of the given anomalies are included.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.234.66.217

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Hofmockel J. and Sax E. (2018). Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 411-416. DOI: 10.5220/0006758004110416

@conference{vehits18,
author={Julia Hofmockel and Eric Sax},
title={Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={411-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006758004110416},
isbn={978-989-758-293-6},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Isolation Forest for Anomaly Detection in Raw Vehicle Sensor Data
SN - 978-989-758-293-6
AU - Hofmockel J.
AU - Sax E.
PY - 2018
SP - 411
EP - 416
DO - 10.5220/0006758004110416

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.